In artificial intelligence, the challenge of maintaining knowledge stability across sequentially evolving tasks and contexts has become increasingly apparent, as language models are deployed in applications requiring prolonged semantic coherence. While traditional training paradigms emphasize rapid adaptation and generalization, they often overlook the essential need for sustained knowledge retention, resulting in semantic drift and inconsistencies over time. Addressing this gap, the novel Residual Knowledge Stability (RKS) framework presented in this study introduces a robust approach for quantifying longterm knowledge retention, offering a systematic evaluation of the model's internal consistency in handling complex and varied task sequences. The framework's design emphasizes autonomous task sequences, employing metrics that detect semantic drift and measure retention accuracy, without requiring human intervention, thereby allowing for scalable, unbiased assessment. Results indicate that RKS reveals key retention patterns, specifically in scenarios with increased task complexity, high domain diversity, and frequent task assignments, with implications for advancing model design strategies to support stable retention across extensive temporal applications. By prioritizing long-term retention in model evaluation, the RKS framework offers valuable insights for enhancing language model resilience, aligning architectural development more closely with the demands of real-world, continuously evolving information ecosystems.